Applied Methods

Chaudhari, Bischak, & Simmons Applied Methods | Dr. Alex Karner Spring ’18

The Impact of Amazon HQ2 on Potential MSAs Aim Conduct a planning related analysis of the effects of Amazon’s HQ2 on potential cities.

Background Amazon is an American company supplying web hosting services, online retail services, video streaming services, and online delivery services. It is currently the world’s largest online retailer. Amazon is currently headquartered in Seattle, Washington, but in September of 2017 it announced its plans to seek a second home in North America.1 This so called ‘HQ2’ will serve as a second headquarters for Amazon and may employ as many as 50,000 and generate over $5 billion in capital expenditures.2 Amazon solicited requests for proposals from any city in North America.

According to the Amazon Request for proposal (RFP), Amazon is seeking a site at the least the four following characteristics:3 •

Metropolitan areas with more than one million people

•

A stable and business-friendly environment

•

Urban or suburban locations with the potential to attract and retain strong technical talent

•

Communities that think big and creatively when considering locations and real estate options

Additionally, Amazon prefers a site that is within 30 minutes’ drive of the population center, proximate to an international airport, has access to mass transit at the site, is large enough to accommodate a 500,000+ sq. ft. building.

After the initial RFP was put out by Amazon the RFP generated at least 238 responses.4 This list was eventually narrowed down to a final list of 20 cities which are: •

Atlanta

•

Austin

•

Boston

“A New Amazon Headquarters Could Be Coming to a City near You.” “Amazon HQ2 RFP.” 3 “Amazon HQ2 RFP.” 4 “Amazon Refuses Arizona’s Cactus as Bidders for HQ2 Climb to 118.”

1 2

Chaudhari, Bischak, & Simmons Applied Methods | Dr. Alex Karner Spring ’18

•

Chicago

•

Columbus

•

Dallas

•

Denver

•

Indianapolis

•

Los Angeles

•

Miami

•

Montgomery County, Maryland

•

Nashville

•

New York City

•

Newark

•

Northern Virginia

•

Pittsburgh

•

Philadelphia

•

Raleigh

•

Washington D.C

As of the writing of this paper Amazon has not announced a winner, but this has not stopped speculation about which city might win. A wide variety of media outlets and betting markets have attempted to predict which cities are the most likely to land HQ2. An informal poll of the various news articles finds that Atlanta and the DC suburbs are consistently ranked among the top choices for landing HQ2.5 Atlanta is considered attractive because of its large international airport, growing tech sector, and its relative affordability as compared to the other cities. The DC suburbs are considered attractive because of the availability of space, their proximity to the nation’s capital and by extension policy makers, and its fairly robust tech sector as positives.6 Generally, according to our informal review of the speculative articles, Toronto and Los Angeles are the least likely to be chosen.

“In Contest for Amazon HQ2, Experts Bet on Atlanta and D.C. Suburbs”; “This City Is Most Likely to Be Named Amazon’s Second Headquarters.”

6 “Researchers.”

5

Chaudhari, Bischak, & Simmons Applied Methods | Dr. Alex Karner Spring ’18

Study Design For the purposes of this report we assumed that HQ2 has the potential to impact a city in at least three different ways. First, demographically the addition of 50,000 people will propagate over time and lead to demographic shifts and growth. The demographic impacts of HQ2 were analyzed using the Hamilton-Perry method and are further detailed in the demographic section.

Second, the introduction of 50,000 people, with an average annual total compensation exceeding $100,000 over the next ten to fifteen years. Finally, the introduction of Amazon HQ2 will undoubtedly impact the local economy of the area in which it locates. We used location quotient analysis to analyze the potential economic impacts of HQ2. It will potentially boost the GDP of the area, but it will also shift the employment mix of the area and potentially generate new basic industries.

Demographic Analysis For the purposes of this study we used the Hamilton-Perry (HP) method of demographic analysis. This method relies on two consecutive US Census to calculate Cohort Change Ratios (CCRs) and Child to Woman Ratios (CWRs).7 These ratios are then multiplied by launch year populations to projection the population in the target year. In tests of the accuracy of the Hamilton-Perry method has been found to accurate and it can be used when cohort component sub-categories are not needed.8 For the purposes of this project 2000 and 2010 US Census data was used to calculate the CCRs and CWRs. The population was than projected forward to the years 2020 and 2030. Two separate projections were performed, first the population as projected out for each of the MSAs in our study, assuming that Amazon HQ2 was not added to the MSA. The results of this are displayed in Figure 1. Than a second set of projections was performed assuming that Amazon HQ2 was added to the metro area and the results are displayed in Figure 2.

Hamilton and Perry, “A Short Method for Projecting Population By Age from One Decennial Census to Another.”

8 Swanson and Tayman, “A Long Term Test of the Accuracy of the Hamilton-Perry Method for Forecasting State Populations by Age.” 7

Chaudhari, Bischak, & Simmons Applied Methods | Dr. Alex Karner Spring ’18

Metro Area Name

Total 2010

Total 2020

Total 2030

Percent Change

Atlanta-Sandy Springs-

5,268,860.00

6,754,954.22

8,741,357.49

29.41%

1,716,289.00

2,327,835.40

3,125,974.04

34.29%

7,559,060.00

9,719,248.31

12,382,941.34

27.41%

9,686,021.00

10,104,884.5

10,371,741.27

2.64%

Marietta, GA Metro Area Austin-Round Rock-San Marcos, TX Metro Area Boston-Cambridge-Quincy, MA-NH Metro Area (all) Chicago-Joliet-Naperville, IL-IN-WI Metro Area (all)

6

Columbus, OH Metro Area

1,836,536.00

2,175,869.92

2,570,142.95

18.12%

Dallas-Fort Worth-

6,371,773.00

7,690,836.09

9,239,822.71

20.14%

3,090,874.00

3,637,212.07

4,222,428.09

16.09%

1,756,241.00

1,931,857.17

2,152,152.43

11.40%

17,877,006.00

19,271,348.9

20,407,530.76

5.90%

Arlington, TX Metro Area Denver-Aurora-Broomfield, CO Metro Area Indianapolis-Carmel, IN Metro Area Los Angeles-Long BeachSanta Ana, CA Metro Area Miami-Fort Lauderdale-

3 5,564,635.00

7,859,825.80

10,916,254.75

38.89%

1,589,934.00

2,030,660.67

2,573,545.83

26.73%

Pompano Beach, FL Metro Area Nashville-Davidson-Murfreesboro--Franklin, TN

Chaudhari, Bischak, & Simmons Applied Methods | Dr. Alex Karner Spring ’18

Metro Area New York-Northern New

22,085,649.00

Jersey-Long Island, NY-NJ-

22,761,278.1

23,130,908.72

1.62%

1

PA Metro Area (all) Philadelphia-Camden-

6,533,683.00

6,849,612.47

7,138,362.61

4.22%

Pittsburgh, PA Metro Area

2,356,285.00

2,333,669.93

2,288,626.97

-1.93%

Raleigh-Cary, NC Metro

1,130,490.00

1,057,675.01

979,883.40

-7.35%

8,572,971.00

9,619,999.80

10,739,409.91

11.64%

Wilmington, PA-NJ-DEMD Metro Area (all)

Area Washington-BaltimoreNorthern Virginia (All) Figure 1: HP Projection with no HQ2 Table 1: Projections for Metro Areas Assuming no HQ2

Metro Area Name

Atlanta-Sandy Springs-Marietta,

Total 2010

5,268,860.00

Total

Total

Percent

2020_HQ2

2030_HQ2

Change

6,870,256.76

8,900,756.6

29.55%

GA Metro Area Austin-Round Rock-San Marcos,

2 1,716,289.00

2,443,137.93

TX Metro Area Boston-Cambridge-Quincy, MANH Metro Area (all)

3,304,030.9

35.24%

8 7,559,060.00

9,834,550.85

12,555,220.

10

27.66%

Chaudhari, Bischak, & Simmons Applied Methods | Dr. Alex Karner Spring ’18

Chicago-Joliet-Naperville, IL-IN- 9,686,021.00

10,220,187.09

WI Metro Area (all) Columbus, OH Metro Area

10,507,491.

2.81%

60 1,836,536.00

2,291,172.46

2,727,427.2

19.04%

4 Dallas-Fort Worth-Arlington, TX 6,371,773.00

7,806,138.62

Metro Area Denver-Aurora-Broomfield, CO

3,090,874.00

3,752,514.61

4,377,218.4

16.65%

6 1,756,241.00

2,047,159.70

Area

2,299,347.0

12.32%

4

Los Angeles-Long Beach-Santa

17,877,006.0

Ana, CA Metro Area

0

Miami-Fort Lauderdale-Pompano 5,564,635.00

19,386,651.46

20,547,542.

5.99%

43 7,975,128.33

Beach, FL Metro Area Nashville-Davidson--

20.35%

5

Metro Area Indianapolis-Carmel, IN Metro

9,394,683.1

11,101,428.

39.20%

87 1,589,934.00

2,145,963.21

Murfreesboro--Franklin, TN

2,742,353.7

27.79%

8

Metro Area New York-Northern New Jersey-

22,085,649.0

Long Island, NY-NJ-PA Metro

0

22,876,580.64

23,267,202.

1.71%

29

Area (all) Philadelphia-Camden-

6,533,683.00

6,964,915.00

Wilmington, PA-NJ-DE-MD

7,274,838.3

4.45%

9

Metro Area (all) Pittsburgh, PA Metro Area

2,356,285.00

2,448,972.46

2,422,876.4 8

-1.07%

Chaudhari, Bischak, & Simmons Applied Methods | Dr. Alex Karner Spring ’18

Raleigh-Cary, NC Metro Area

1,130,490.00

1,172,977.54

1,101,388.7

-6.10%

8 Washington-Baltimore-Northern

8,572,971.00

9,735,302.34

Virginia (All)

10,885,636.

11.82%

27 Figure 2: Projections assuming HQ2

In order to make assumptions about the effects of HQ2 a number of assumptions and data sources were used. First, we made several simplifying assumptions namely that no interracial marriages took place, that white people have white children and non-white people have nonwhite children, and that everyone is married to someone in their same age cohort. We assumed that HQ2 jobs were mostly concentrated in the high-tech sector. Thus, using EEOC data we concluded that 68.5% of the employees were white of the 50,000 new employees are likely to be white and 31.5% of the employees were non-white.9 Additionally, using the same data we assumed that 64% of the employees are likely to be male and 36% of the employees are likely to be women.10 Additionally, in order to make assumptions about the ages of employees we used a median age of 31, which is the median age of an Amazon employee.11 Finally, we made assumptions about the size of families and the likelihood of the employees being married.

According to Census data about 51% of males who are 15 or older are married and 47.7% of females are married. Thus, we concluded that about 27,398 of the employees are likely to be married. We assumed an average number of children of 1.9 which is roughly the size of the average American family unit.12 This resulted in a total number of children of 47,625.40. We evenly distributed the children across the four age groups that constitute children. The results of this portion of the analysis are summarized in Figure 3.

Male Employees

32,000.00

Female Employees

18,000.00

“Diversity in High Tech.”

“Diversity in High Tech.”

11 Hartmans, “The Average Age of Employees at All the Top Tech Companies, in One Chart.”

12 “The World Factbook — Central Intelligence Agency.”

9

10

Chaudhari, Bischak, & Simmons Applied Methods | Dr. Alex Karner Spring ’18

White Male Employees

21,920.00

White Female Employees

12,330.00

Non-White Male Employees

10,080.00

Non-White Female Employees

5,670.00

Total Children

47,625.40

Total White Male Children

16,311.70

Total White Female Children

16,311.70

Total Non-White Male

7,501.00

Children Total Non-White Female

7,501.00

Children Figure 3: HQ2 Assumptions Overall, it appears that Amazon HQ2 will cause a .51% change in the growth rates of the cities concerned. Figure 4 summarizes the findings. The impact appears to be the largest for smaller MSAs.

Metro Area Name

Percent

Percent

Percent

Change_HQ2

Change_NH

Difference

Q2 Atlanta-Sandy Springs-Marietta, GA Metro Area

29.55%

29.41%

0.15%

Chaudhari, Bischak, & Simmons Applied Methods | Dr. Alex Karner Spring ’18

Austin-Round Rock-San Marcos, TX

35.24%

34.29%

0.95%

27.66%

27.41%

0.26%

2.81%

2.64%

0.17%

Columbus, OH Metro Area

19.04%

18.12%

0.92%

Dallas-Fort Worth-Arlington, TX Metro

20.35%

20.14%

0.21%

16.65%

16.09%

0.56%

Indianapolis-Carmel, IN Metro Area

12.32%

11.40%

0.92%

Los Angeles-Long Beach-Santa Ana, CA

5.99%

5.90%

0.09%

39.20%

38.89%

0.31%

27.79%

26.73%

1.06%

1.71%

1.62%

0.08%

Philadelphia-Camden-Wilmington, PA-NJ- 4.45%

4.22%

0.23%

Metro Area Boston-Cambridge-Quincy, MA-NH Metro Area (all) Chicago-Joliet-Naperville, IL-IN-WI Metro Area (all)

Area Denver-Aurora-Broomfield, CO Metro Area

Metro Area Miami-Fort Lauderdale-Pompano Beach, FL Metro Area Nashville-Davidson--Murfreesboro-Franklin, TN Metro Area New York-Northern New Jersey-Long Island, NY-NJ-PA Metro Area (all)

DE-MD Metro Area (all) Pittsburgh, PA Metro Area

-1.07%

-1.93%

0.86%

Raleigh-Cary, NC Metro Area

-6.10%

-7.35%

1.25%

Washington-Baltimore-Northern Virginia

11.82%

11.64%

0.18%

Average

0.51%

(All)

Figure 4: Percent Change from HQ2

Chaudhari, Bischak, & Simmons Applied Methods | Dr. Alex Karner Spring ’18

Housing We know that coastal cities in the United States, especially West and North east currently pay more in rents. Zillow is an online real estate database company and below are the forecasts it made for increase in rents due to HQ2.13

Figure 5: The Amazon Effect on Rents by Zillow Several studies indicate that that comparatively smaller metropolitan areas (populationwise) like Nashville, Denver, Raleigh, Pittsburgh, Miami will see higher impacts due to the HQ2.

These cities are currently undergoing rapid development and are not prepared for a sudden demand in housing units. Other cities like Los Angeles (1.9% increase) and Boston (1.4% increase) may be an exception to this trend due to their ongoing struggles with lack of affordable housing.14

“Amazon Effect: Nashville and Denver Likely to See Biggest Rent Hikes If Chosen for HQ2 - Zillow Research.”

14 Reporter, “Can Boston’s Affordable Housing Shortage Be Eased?”; Hiltzik, “California’s Housing Crisis Reaches from the Homeless to the Middle Class — but It’s Still Almost Impossible to Fix.” 13

Chaudhari, Bischak, & Simmons Applied Methods | Dr. Alex Karner Spring ’18

There are relatively less forecasted impacts on housing markets in cities like Washington, Indianapolis, Atlanta, Philadelphia. This suggests that these cities might have more available unoccupied housing units or provisions of affordable housing programs. Cities with lower increase in rents might also have more available diversified labor force. This ensures that there won't be a very high demand for relocation. Such cities are also less likely to see a wage gap between incoming and existing workers and hence will see the least adverse effects on the low and middle-income people.15 The Government standard for housing-cost burden considers housing costs as a share of incomes (for both renters and owners) and divides cost-burdened houses into two categories: 1. Cost-burdened households pay more than 30% of income for housing including utilities.

2. Severely cost-burdened households pay more than 50% of income towards housing.

It is not a surprise that MSAs like New York, Los Angeles, Chicago, Miami, Boston are already severely burdened in terms of housing costs. These cities with higher rent costs are making the process of saving for a down payment very difficult. If such cities (like Miami, Los Angeles) are also forecasted to have high increase in rents, this may see adverse effects of displacement.

On the other hand, Raleigh, Nashville, Denver, Dallas, Pittsburgh have a significantly lower housing-cost burden. A boost in the real estate market is generally considered beneficial for the economy. However sudden hikes in rents/ mortgages puts a strain on the housing markets and can have adverse effects in form of displacement. If there are significant investments in transportation infrastructure, people can save transportation costs and use it for housing, save for down payments etc.

“Amazon Effect: Nashville and Denver Likely to See Biggest Rent Hikes If Chosen for HQ2 - Zillow Research.�

15

Chaudhari, Bischak, & Simmons Applied Methods | Dr. Alex Karner Spring ’18

Figure 6: Summary of forecasted increase in rents and share of burdened housing costs in each MSA Source: Harvard University

Transit infrastructure One of Amazon’s requirement is also a reliable public transport system and productive housing-jobs commute. Transit and housing can mutually benefit each other to offset living costs in the MSA. We did a quantitative as well as a qualitative assessment of the existing and proposed transportation infrastructure in the MSAs.

Chaudhari, Bischak, & Simmons Applied Methods | Dr. Alex Karner Spring ’18

Figure 7: Summary of transit scores and ridership per capita in each MSA Source: Walkscore.com “Transit score is a patented measure of how well a location is served by public transit.

Transit Score is based on data released in a standard format by public transit agencies. To calculate a Transit Score, we assign a "usefulness" value to nearby transit routes based on the frequency, type of route (rail, bus, etc.), and distance to the nearest stop on the route.”16 A population-weighted methodology is used to compute the average for a given city.

Ridership per capita can be calculated using the total number of unlinked passenger trips/ population of MSA. MSAs like New York, Chicago, Philadelphia, Boston, DC that have impressive public transit, legacy subway systems and high ridership. Cities with a low transit score also have corresponding low ridership. We notice that there is potential in cities like Denver and Los Angeles that have high ridership even with a moderate transit score. Transit investments that support people who already ride transit will have meaningful ridership gains in the future. Below are some proposed investments for transit infrastructure in these MSAs.

16

“Walk Score Methodology.”

Chaudhari, Bischak, & Simmons Applied Methods | Dr. Alex Karner Spring ’18

1. Los Angeles: California High-Speed Rail Authority released its Draft 2018 Business Plan. The plan sets aside budgets for a high-speed rail connecting major cities like Los Angeles, San Francisco, Sacramento and San Diego.17 2. Nashville: The city recently voted against a 5.4 billion transit plan called ‘Let’s move Nashville’ for five new light rail lines totaling 26 miles, a 1.8-mile downtown transit tunnel, 19 transit centers around the city, four new bus rapid transit lines, four new crosstown bus routes, and a slew of signal, sidewalk, and bike infrastructure improvements. This plan is set be revised and alternatives will be proposed soon.18 3. Denver: As a part of transit improvements, RTD and Denver Public works department are planning to adopt smart technologies like “transit signal priority.” on 17 intersections.

This will It enables approaching buses to communicate with traffic signals and eventually save a huge chunk of commute time for its residents19

Economy Amazon purports to have provided a significant boost to Seattle’s economy with the development of its headquarters starting in the late 2000s. We conducted a Location Quotient Analysis to test these claims, and determine what cities are in most need of such an economic boost. To match population projection data used in the population projections, the 2010 County Business Patterns data was downloaded from the Census Bureau. The most up to date NAICS codes for that year were the 2007 codes. Four-digit NAICS codes were used for the calculation of location quotients. Data from 2015, thus 2012 NAICS codes, was used with Seattle as well for comparative purposes.

Once the basic industries were determined, a ratio of the total number of basic industries to the total number of all industries was calculated. This ratio is meant to gage the “import of income” to an MSA that basic industries can provide.20 A higher percentage is an indicator of a healthy economy that can “export jobs, retain substantial income, and build internal linkages.”21 As shown in Table M1, most MSAs had a ratio of 37-39%, a few more in the 30s and two

“American Planning Association.”

“What’s Next?”

19 “RTD, Denver Public Works Prep to Speed Up Buses on Six City Streets.”

20 Leigh and Blakely, Planning Local Economic Development.

21 Leigh and Blakely.

17 18

Chaudhari, Bischak, & Simmons Applied Methods | Dr. Alex Karner Spring ’18

outside the 30s. Based on this measure the Washington D.C. MSA economy was struggling the most with a ratio of 26% and the Chicago MSA economy was the healthiest with a ratio of 44%.

The Seattle MSA economy ratio grew from 34% to 465. All this growth cannot be attributed solely to Amazon, but there is no doubt they played a big role in it. Because location quotients are calculated relative to how big a sector is in the U.S., a sector’s relative importance could mask its local importance.

To make sure the true local importance is observed, a ratio of basic industry jobs to population, defined for every 100,000 people in an MSA, was also calculated. All but one MSA had between 20,000 and 30,000 basic industry jobs per 100,000 people. The Los Angeles MSA has by far the lowest, at 2,446. Its basic industry ratio is similar to the others observed, but the disparity comes from its basic industries having mostly thousands of jobs, instead of tens of thousands in the case of the New York MSA. Its basic industries, like the inland water transportation industry and manufacturing industries have a lot of jobs relative to the entire U.S but do not have a lot of jobs overall. Seattle was below the normal range in 2010 but grew from 19,559 to 32,894 jobs per 100,000 people by 2015.

Additionally, it is useful to look specifically at the location quotient of the NAICS industry sector Amazon is listed under, 4541 - Electronic Shopping and Mail-Order Houses. Evaluating location quotient from the perspective of bidding cities would be different than if it was evaluated from Amazon’s perspective. Too low of a quotient could indicate that Amazon could fill a void in an MSA economy. However, a high quotient could mean that an MSA is primed for Amazon’s success there due to agglomeration economies. We focus on what would be best for the MSAs. Columbus, with by far the highest quotient, absolutely does not need a boost in this sector. Washington D.C., the MSA with the lowest basic industry ratio, and Los Angeles, the MSA with the lowest number of basic industry jobs per 100,000 people, both have location quotients less than one for NAICS 4541. In fact, these two MSAs have the lowest quotients and could use the boost in this sector that Amazon would provide. Seattle’s electronic shopping and mail-order house industry location quotient grew from 0.980 to 1.441 between 2010 and 2015.

With tens of thousands of jobs incoming, it is safe to say that Amazon could flip this sector on its head, making the industry basic, or close to it, in either of these two MSAs.

Chaudhari, Bischak, & Simmons Applied Methods | Dr. Alex Karner Spring ’18

Figure 8: Summary of the Economies of Each MSA

Takeaways For cities that are severely burdened by housing costs, public utilities, and infrastructure will be considerably affected without significant transit investments plans by the regional authorities. Amazon’s HQ2 will not put much of a strain on MSAs that have already high housing markets, like New York, Philadelphia, Chicago. But MSAs like Nashville can expect meaningful boost in its real estate market if its ambitious transit plans are revised, supported and implemented. Economically, Washington, D.C. and Los Angeles are in most need of a boost to their basic sector. Either more basic sector jobs are needed or basic industries with a lot of jobs are needed. Our bottom line recommendation for HQ2 is the DC MSA area.

Chaudhari, Bischak, & Simmons Applied Methods | Dr. Alex Karner Spring ’18

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Chaudhari, Bischak, & Simmons Applied Methods | Dr. Alex Karner Spring ’18

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